AIMC Topic: Deep Learning

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A review of deep learning approaches for multimodal image segmentation of liver cancer.

Journal of applied clinical medical physics
This review examines the recent developments in deep learning (DL) techniques applied to multimodal fusion image segmentation for liver cancer. Hepatocellular carcinoma is a highly dangerous malignant tumor that requires accurate image segmentation f...

Ranking attention multiple instance learning for lymph node metastasis prediction on multicenter cervical cancer MRI.

Journal of applied clinical medical physics
PURPOSE: In the current clinical diagnostic process, the gold standard for lymph node metastasis (LNM) diagnosis is histopathological examination following surgical lymphadenectomy. Developing a non-invasive and preoperative method for predicting LNM...

Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification.

Medical physics
BACKGROUND: The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to evaluate multiple new therapeutic drugs for high-risk breast cancer. Previous studies suggest that pathological complete response (pCR) is a viable indic...

Effect of childhood atropine treatment on adult choroidal thickness using sequential deep learning-enabled segmentation.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
PURPOSE: To describe choroidal thickness measurements using a sequential deep learning segmentation in adults who received childhood atropine treatment for myopia control.

Artificial intelligence for detection and characterization of focal hepatic lesions: a review.

Abdominal radiology (New York)
Focal liver lesions (FLL) are common incidental findings in abdominal imaging. While the majority of FLLs are benign and asymptomatic, some can be malignant or pre-malignant, and need accurate detection and classification. Current imaging techniques,...

A deep learning approach for non-invasive Alzheimer's monitoring using microwave radar data.

Neural networks : the official journal of the International Neural Network Society
Over 50 million people globally suffer from Alzheimer's disease (AD), emphasizing the need for efficient, early diagnostic tools. Traditional methods like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans are expensive, bulky, and s...

MG-Net: A fetal brain tissue segmentation method based on multiscale feature fusion and graph convolution attention mechanisms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Fetal brain tissue segmentation provides foundational support for comprehensively understanding the neurodevelopment of normal and congenital disease-affected fetuses. Manual labeling is very time-consuming, and automated se...

HIV-1 M group subtype classification using deep learning approach.

Computers in biology and medicine
Traditionally, the classification of HIV-1 M group subtypes has depended on statistical methods constrained by sample sizes. Here HIV-1-M-SPBEnv was proposed as the first deep learning-based method for classifying HIV-1 M group subtypes via env gene ...

Deep Learning Detection of Hand Motion During Microvascular Anastomosis Simulations Performed by Expert Cerebrovascular Neurosurgeons.

World neurosurgery
OBJECTIVE: Deep learning enables precise hand tracking without the need for physical sensors, allowing for unsupervised quantitative evaluation of surgical motion and tasks. We quantitatively assessed the hand motions of experienced cerebrovascular n...

Ensemble approach of deep learning models for binary and multiclass classification of histopathological images for breast cancer.

Pathology, research and practice
Breast cancer (BC) is the most frequently occurring cancer disease observed in women after lung cancer. Out of different stages, invasive ductal BC causes maximum deaths in women. In this work, three deep learning (DL) models such as Vision Transform...